32 citations · 87 across the 10 of their papers we have counts for
11 papers
DoraCycle: Domain-Oriented Adaptation of Unified Generative Model in Multimodal Cycles
Rui Zhao, Weijia Mao, Mike Zheng Shou
Adapting generative models to specific domains presents an effective solution for satisfying specialized requirements. However, adapting to some complex domains remains challenging…
DreamVideo-2: Zero-Shot Subject-Driven Video Customization with Precise Motion Control
Yujie Wei, Shiwei Zhang, Hangjie Yuan +9
Recent advances in customized video generation have enabled users to create videos tailored to both specific subjects and motion trajectories. However, existing methods often requi…
VideoSwap: Customized Video Subject Swapping with Interactive Semantic Point Correspondence
Yuchao Gu, Yipin Zhou, Bichen Wu +7
Current diffusion-based video editing primarily focuses on structure-preserved editing by utilizing various dense correspondences to ensure temporal consistency and motion alignmen…
X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model
Lingmin Ran, Xiaodong Cun, Jia-Wei Liu +5
We introduce X-Adapter, a universal upgrader to enable the pretrained plug-and-play modules (e.g., ControlNet, LoRA) to work directly with the upgraded text-to-image diffusion mode…
MotionDirector: Motion Customization of Text-to-Video Diffusion Models
Rui Zhao, Yuchao Gu, Jay Zhangjie Wu +5
Large-scale pre-trained diffusion models have exhibited remarkable capabilities in diverse video generations. Given a set of video clips of the same motion concept, the task of Mot…
DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing
Jia-Wei Liu, Yan-Pei Cao, Jay Zhangjie Wu +6
Despite recent progress in diffusion-based video editing, existing methods are limited to short-length videos due to the contradiction between long-range consistency and frame-wise…
Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation
David Junhao Zhang, Jay Zhangjie Wu, Jia-Wei Liu +5
Significant advancements have been achieved in the realm of large-scale pre-trained text-to-video Diffusion Models (VDMs). However, previous methods either rely solely on pixel-bas…
Zero-shot Text-driven Physically Interpretable Face Editing
Yapeng Meng, Songru Yang, Xu Hu +4
This paper proposes a novel and physically interpretable method for face editing based on arbitrary text prompts. Different from previous GAN-inversion-based face editing methods t…
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
Weijia Wu, Yuzhong Zhao, Hao Chen +6
Current deep networks are very data-hungry and benefit from training on largescale datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data can…
Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models
Yuchao Gu, Xintao Wang, Jay Zhangjie Wu +10
Public large-scale text-to-image diffusion models, such as Stable Diffusion, have gained significant attention from the community. These models can be easily customized for new con…
Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation
Rui Zhao, Wei Li, Zhipeng Hu +4
Recent popular Role-Playing Games (RPGs) saw the great success of character auto-creation systems. The bone-driven face model controlled by continuous parameters (like the position…